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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64470
Volume:
10 |
6 |
Jun 2026
Pages:
1-7
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
FIRE TECHNOLOGY & SAFETY ENGINEERING
VIKRANT UNIVERSITY, GWALIOR
FIRE TECHNOLOGY & SAFETY ENGINEERING
VIKRANT UNIVERSITY, GWALIOR
The rapid advancement of Industry 4.0 technologies has transformed conventional engineering design methodologies into intelligent, data-driven systems. Digital Twin (DT) technology, combined with Deep Learning (DL), provides a powerful framework for real-time monitoring, predictive analysis, and adaptive optimization of engineering components. Structural and mechanical joints are critical elements in engineering systems, and their failure often leads to severe economic losses, safety hazards, and operational disruptions. This research proposes a Deep Learning-Assisted Digital Twin Architecture for Smart Joint Design and Failure Prevention. The framework integrates sensor-based data acquisition, finite element modeling, cloud computing, Internet of Things (IoT), and deep neural networks to create a dynamic virtual representation of physical joints. The proposed architecture continuously analyzes operational conditions, predicts potential failures, and recommends design modifications for performance enhancement. The study demonstrates that deep learning-enabled digital twins can improve failure prediction accuracy by more than 90%, reduce maintenance costs by approximately 25–40%, and extend component service life by nearly 30%. The proposed model offers a significant contribution toward intelligent manufacturing, predictive maintenance, and sustainable engineering design.
KEYWORDS
This study presented a Deep Learning-Assisted Digital Twin Architecture for Smart Joint Design and Failure Prevention. The proposed framework integrates IoT sensing, Digital Twin modeling, Deep Learning analytics, and decision-support mechanisms to create an intelligent engineering ecosystem. The architecture enables continuous monitoring, predictive maintenance, adaptive design optimization, and proactive failure prevention. Simulation results demonstrated significant improvements in prediction accuracy, reliability enhancement, and lifecycle cost reduction. The proposed framework has strong potential for applications in mechanical engineering, civil infrastructure, aerospace systems, automotive manufacturing, and smart industrial environments.
1. Fu, Y., Zhu, G., Zhu, M., et al. (2022). Digital Twin for Integration of Design-Manufacturing-Maintenance: An Overview. Chinese Journal of Mechanical Engineering, 35(80). 2. Van Dinter, R., Tekinerdogan, B., & Catal, C. (2022). Predictive Maintenance Using Digital Twins: A Systematic Literature Review. Information and Software Technology, 151, 107008. 3. Ritto, T.G., & Rochinha, F.A. (2020). Digital Twin, Physics-Based Model, and Machine Learning Applied to Damage Detection in Structures. 4. Torzoni, M., Tezzele, M., Mariani, S., Manzoni, A., & Willcox, K.E. (2023). A Digital Twin Framework for Civil Engineering Structures. 5. Li, S., & Brennan, F. (2024). Digital Twin Enabled Structural Integrity Management: Critical Review and Framework Development. 6. Nele, L., Mattera, G., Yap, E.W., et al. (2024). Towards the Application of Machine Learning in Digital Twin Technology: A Multi-Scale Review. 7. Islam, M.R., Subramaniam, M., & Huang, P.C. (2025). Image-Based Deep Learning for Smart Digital Twins: A Review. 8. Lehner, D., Zhang, J., Pfeiffer, J., et al. (2025). Model-Driven Engineering for Digital Twins: A Systematic Mapping Study. 9. CIRP Manufacturing Research Group. (2025). Developing and Leveraging Digital Twins in Engineering Design. 10. Ali, A., Ali, S., Ahmed, F., et al. (2026). Predictive Maintenance of Civil Infrastructure Using Digital Twin Technology and AI-Based Structural Performance Modeling Techniques.
Publication Date
6/3/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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